Partitioning to uncover conditions for permutation tests to control multiple testing error rates.
This article discusses specific assumptions necessary for permutation multiple tests to control the Familywise Error Rate (FWER). At issue is that, in comparing parameters of the marginal distributions of two sets of multivariate observations, validity of permutation testing is affected by all the parameters in the joint distributions of the observations. We show the surprising fact that, in the case of a linear model with i.i.d. errors such as in the analysis of Quantitative Trait Loci (QTL), this issue has no impact on control of FWER, if the test statistic is of a particular form. On the other hand, in the analysis of gene expression levels or multiple safety endpoints, unless some assumption connecting the marginal distributions of the observations to their joint distributions is made, permutation multiple tests may not control FWER.
Calian
Violeta
V
Science Institute, University of Iceland, Dunhaga 3, 107 Reykjavik, Iceland.
Li
Dongmei
D
Hsu
Jason C
JC
D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D023381:Endpoint Determination / Q000706:statistics & numerical data; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D040641:Quantitative Trait Loci